Overview

Dataset statistics

Number of variables14
Number of observations500
Missing cells18
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.6 KiB
Average record size in memory120.0 B

Variable types

Categorical5
Numeric9

Warnings

Name has a high cardinality: 465 distinct values High cardinality
Club has a high cardinality: 98 distinct values High cardinality
Nation has a high cardinality: 58 distinct values High cardinality
League is highly correlated with ClubHigh correlation
Club is highly correlated with LeagueHigh correlation
Name is uniformly distributed Uniform

Reproduction

Analysis started2021-03-30 14:46:18.053258
Analysis finished2021-03-30 14:46:27.528387
Duration9.48 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct465
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
Daniele Rugani
 
3
Márcio Rafael Ferreira de Souza
 
2
Diego Perotti
 
2
Felipe Anderson Pereira Gomes
 
2
Rúben Santos Gato Alves Dias
 
2
Other values (460)
489 

Length

Max length35
Median length15
Mean length17.152
Min length8

Characters and Unicode

Total characters8576
Distinct characters92
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique431 ?
Unique (%)86.2%

Sample

1st rowLionel Messi
2nd rowC. Ronaldo dos Santos Aveiro
3rd rowNeymar da Silva Santos Jr.
4th rowKevin De Bruyne
5th rowRobert Lewandowski
ValueCountFrequency (%)
Daniele Rugani3
 
0.6%
Márcio Rafael Ferreira de Souza2
 
0.4%
Diego Perotti2
 
0.4%
Felipe Anderson Pereira Gomes2
 
0.4%
Rúben Santos Gato Alves Dias2
 
0.4%
Morgan Sanson2
 
0.4%
Alejandro Gómez2
 
0.4%
Douglas Costa de Souza2
 
0.4%
Arkadiusz Milik2
 
0.4%
Gonzalo Higuaín2
 
0.4%
Other values (455)479
95.8%
2021-03-30T22:46:27.736439image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de23
 
1.8%
silva19
 
1.5%
da13
 
1.0%
josé12
 
0.9%
santos10
 
0.8%
lucas9
 
0.7%
souza7
 
0.5%
sergio7
 
0.5%
garcía7
 
0.5%
dos6
 
0.5%
Other values (861)1169
91.2%

Most occurring characters

ValueCountFrequency (%)
a885
 
10.3%
782
 
9.1%
e688
 
8.0%
r581
 
6.8%
o580
 
6.8%
i579
 
6.8%
n488
 
5.7%
l391
 
4.6%
s344
 
4.0%
d230
 
2.7%
Other values (82)3028
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6533
76.2%
Uppercase Letter1246
 
14.5%
Space Separator782
 
9.1%
Other Punctuation8
 
0.1%
Dash Punctuation7
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a885
13.5%
e688
10.5%
r581
 
8.9%
o580
 
8.9%
i579
 
8.9%
n488
 
7.5%
l391
 
6.0%
s344
 
5.3%
d230
 
3.5%
u229
 
3.5%
Other values (43)1538
23.5%
ValueCountFrequency (%)
M140
 
11.2%
S110
 
8.8%
A104
 
8.3%
J74
 
5.9%
R71
 
5.7%
G68
 
5.5%
L67
 
5.4%
D67
 
5.4%
C64
 
5.1%
B62
 
5.0%
Other values (25)419
33.6%
ValueCountFrequency (%)
.7
87.5%
'1
 
12.5%
ValueCountFrequency (%)
782
100.0%
ValueCountFrequency (%)
-7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7779
90.7%
Common797
 
9.3%

Most frequent character per script

ValueCountFrequency (%)
a885
 
11.4%
e688
 
8.8%
r581
 
7.5%
o580
 
7.5%
i579
 
7.4%
n488
 
6.3%
l391
 
5.0%
s344
 
4.4%
d230
 
3.0%
u229
 
2.9%
Other values (78)2784
35.8%
ValueCountFrequency (%)
782
98.1%
.7
 
0.9%
-7
 
0.9%
'1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8301
96.8%
None275
 
3.2%

Most frequent character per block

ValueCountFrequency (%)
a885
 
10.7%
782
 
9.4%
e688
 
8.3%
r581
 
7.0%
o580
 
7.0%
i579
 
7.0%
n488
 
5.9%
l391
 
4.7%
s344
 
4.1%
d230
 
2.8%
Other values (46)2753
33.2%
ValueCountFrequency (%)
é66
24.0%
í43
15.6%
á41
14.9%
ć23
 
8.4%
ú13
 
4.7%
ó11
 
4.0%
ñ9
 
3.3%
ã9
 
3.3%
ü7
 
2.5%
Á6
 
2.2%
Other values (26)47
17.1%

Club
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct98
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
Atlético de Madrid
 
22
Real Madrid
 
20
Piemonte Calcio
 
18
Manchester City
 
17
Tottenham Hotspur
 
17
Other values (93)
406 

Length

Max length27
Median length13
Mean length13.004
Min length3

Characters and Unicode

Total characters6502
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)6.6%

Sample

1st rowFC Barcelona
2nd rowPiemonte Calcio
3rd rowParis Saint-Germain
4th rowManchester City
5th rowBayern München
ValueCountFrequency (%)
Atlético de Madrid22
 
4.4%
Real Madrid20
 
4.0%
Piemonte Calcio18
 
3.6%
Manchester City17
 
3.4%
Tottenham Hotspur17
 
3.4%
FC Barcelona16
 
3.2%
Bayern München16
 
3.2%
Inter16
 
3.2%
Liverpool15
 
3.0%
Chelsea15
 
3.0%
Other values (88)328
65.6%
2021-03-30T22:46:27.963011image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
madrid42
 
4.4%
fc38
 
3.9%
de37
 
3.8%
real36
 
3.7%
manchester31
 
3.2%
city26
 
2.7%
united23
 
2.4%
atlético22
 
2.3%
cf20
 
2.1%
borussia20
 
2.1%
Other values (152)668
69.4%

Most occurring characters

ValueCountFrequency (%)
e647
 
10.0%
a602
 
9.3%
463
 
7.1%
i461
 
7.1%
n382
 
5.9%
r381
 
5.9%
t371
 
5.7%
l332
 
5.1%
o329
 
5.1%
s223
 
3.4%
Other values (54)2311
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4930
75.8%
Uppercase Letter1055
 
16.2%
Space Separator463
 
7.1%
Decimal Number27
 
0.4%
Other Punctuation14
 
0.2%
Dash Punctuation13
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e647
13.1%
a602
12.2%
i461
9.4%
n382
 
7.7%
r381
 
7.7%
t371
 
7.5%
l332
 
6.7%
o329
 
6.7%
s223
 
4.5%
d206
 
4.2%
Other values (22)996
20.2%
ValueCountFrequency (%)
C151
14.3%
M114
10.8%
B112
10.6%
S82
 
7.8%
L75
 
7.1%
F73
 
6.9%
A70
 
6.6%
R56
 
5.3%
P52
 
4.9%
G47
 
4.5%
Other values (12)223
21.1%
ValueCountFrequency (%)
09
33.3%
49
33.3%
94
14.8%
13
 
11.1%
82
 
7.4%
ValueCountFrequency (%)
'7
50.0%
.5
35.7%
&2
 
14.3%
ValueCountFrequency (%)
463
100.0%
ValueCountFrequency (%)
-13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5985
92.0%
Common517
 
8.0%

Most frequent character per script

ValueCountFrequency (%)
e647
 
10.8%
a602
 
10.1%
i461
 
7.7%
n382
 
6.4%
r381
 
6.4%
t371
 
6.2%
l332
 
5.5%
o329
 
5.5%
s223
 
3.7%
d206
 
3.4%
Other values (44)2051
34.3%
ValueCountFrequency (%)
463
89.6%
-13
 
2.5%
09
 
1.7%
49
 
1.7%
'7
 
1.4%
.5
 
1.0%
94
 
0.8%
13
 
0.6%
82
 
0.4%
&2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII6446
99.1%
None56
 
0.9%

Most frequent character per block

ValueCountFrequency (%)
e647
 
10.0%
a602
 
9.3%
463
 
7.2%
i461
 
7.2%
n382
 
5.9%
r381
 
5.9%
t371
 
5.8%
l332
 
5.2%
o329
 
5.1%
s223
 
3.5%
Other values (48)2255
35.0%
ValueCountFrequency (%)
é29
51.8%
ü16
28.6%
ş6
 
10.7%
ç3
 
5.4%
ó1
 
1.8%
ã1
 
1.8%

League
Categorical

HIGH CORRELATION

Distinct19
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
England Premier League
125 
Spain Primera División
115 
Italy Serie A
81 
Germany 1. Bundesliga
65 
France Ligue 1
30 
Other values (14)
84 

Length

Max length26
Median length22
Mean length19.452
Min length13

Characters and Unicode

Total characters9726
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st rowSpain Primera División
2nd rowItaly Serie A
3rd rowFrance Ligue 1
4th rowEngland Premier League
5th rowGermany 1. Bundesliga
ValueCountFrequency (%)
England Premier League125
25.0%
Spain Primera División115
23.0%
Italy Serie A81
16.2%
Germany 1. Bundesliga65
13.0%
France Ligue 130
 
6.0%
Portugal Primeira Liga20
 
4.0%
China Super League15
 
3.0%
Holland Eredivisie8
 
1.6%
Turkey Süper Lig8
 
1.6%
Ukraine Premier Liga6
 
1.2%
Other values (9)27
 
5.4%
2021-03-30T22:46:28.158688image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
league155
 
10.4%
premier131
 
8.8%
england126
 
8.5%
división116
 
7.8%
primera116
 
7.8%
spain115
 
7.7%
195
 
6.4%
italy81
 
5.4%
a81
 
5.4%
serie81
 
5.4%
Other values (32)390
26.2%

Most occurring characters

ValueCountFrequency (%)
e1142
11.7%
i1032
 
10.6%
987
 
10.1%
a872
 
9.0%
r806
 
8.3%
n680
 
7.0%
g432
 
4.4%
m333
 
3.4%
l308
 
3.2%
u302
 
3.1%
Other values (38)2832
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7126
73.3%
Uppercase Letter1448
 
14.9%
Space Separator987
 
10.1%
Decimal Number96
 
1.0%
Other Punctuation69
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
e1142
16.0%
i1032
14.5%
a872
12.2%
r806
11.3%
n680
9.5%
g432
 
6.1%
m333
 
4.7%
l308
 
4.3%
u302
 
4.2%
d218
 
3.1%
Other values (14)1001
14.0%
ValueCountFrequency (%)
P293
20.2%
S235
16.2%
L230
15.9%
E143
9.9%
D116
 
8.0%
A91
 
6.3%
I81
 
5.6%
B70
 
4.8%
G69
 
4.8%
F33
 
2.3%
Other values (10)87
 
6.0%
ValueCountFrequency (%)
.65
94.2%
'4
 
5.8%
ValueCountFrequency (%)
987
100.0%
ValueCountFrequency (%)
196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8574
88.2%
Common1152
 
11.8%

Most frequent character per script

ValueCountFrequency (%)
e1142
13.3%
i1032
12.0%
a872
 
10.2%
r806
 
9.4%
n680
 
7.9%
g432
 
5.0%
m333
 
3.9%
l308
 
3.6%
u302
 
3.5%
P293
 
3.4%
Other values (34)2374
27.7%
ValueCountFrequency (%)
987
85.7%
196
 
8.3%
.65
 
5.6%
'4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9602
98.7%
None124
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
e1142
11.9%
i1032
 
10.7%
987
 
10.3%
a872
 
9.1%
r806
 
8.4%
n680
 
7.1%
g432
 
4.5%
m333
 
3.5%
l308
 
3.2%
u302
 
3.1%
Other values (36)2708
28.2%
ValueCountFrequency (%)
ó116
93.5%
ü8
 
6.5%

Nation
Categorical

HIGH CARDINALITY

Distinct58
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
Spain
82 
Brazil
59 
France
55 
Germany
35 
Argentina
27 
Other values (53)
242 

Length

Max length24
Median length6
Mean length6.714
Min length4

Characters and Unicode

Total characters3357
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)4.4%

Sample

1st rowArgentina
2nd rowPortugal
3rd rowBrazil
4th rowBelgium
5th rowPoland
ValueCountFrequency (%)
Spain82
16.4%
Brazil59
11.8%
France55
 
11.0%
Germany35
 
7.0%
Argentina27
 
5.4%
Portugal27
 
5.4%
England24
 
4.8%
Italy23
 
4.6%
Holland15
 
3.0%
Belgium15
 
3.0%
Other values (48)138
27.6%
2021-03-30T22:46:28.394090image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spain82
15.7%
brazil59
 
11.3%
france55
 
10.6%
germany35
 
6.7%
portugal27
 
5.2%
argentina27
 
5.2%
england24
 
4.6%
italy23
 
4.4%
belgium15
 
2.9%
holland15
 
2.9%
Other values (57)159
30.5%

Most occurring characters

ValueCountFrequency (%)
a480
14.3%
n347
 
10.3%
r289
 
8.6%
i267
 
8.0%
e233
 
6.9%
l228
 
6.8%
g122
 
3.6%
o118
 
3.5%
S107
 
3.2%
t106
 
3.2%
Other values (37)1060
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2813
83.8%
Uppercase Letter520
 
15.5%
Space Separator21
 
0.6%
Other Punctuation3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a480
17.1%
n347
12.3%
r289
10.3%
i267
9.5%
e233
8.3%
l228
8.1%
g122
 
4.3%
o118
 
4.2%
t106
 
3.8%
p88
 
3.1%
Other values (15)535
19.0%
ValueCountFrequency (%)
S107
20.6%
B77
14.8%
F55
10.6%
G42
 
8.1%
A39
 
7.5%
P35
 
6.7%
C33
 
6.3%
I29
 
5.6%
E25
 
4.8%
H19
 
3.7%
Other values (10)59
11.3%
ValueCountFrequency (%)
21
100.0%
ValueCountFrequency (%)
'3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3333
99.3%
Common24
 
0.7%

Most frequent character per script

ValueCountFrequency (%)
a480
14.4%
n347
 
10.4%
r289
 
8.7%
i267
 
8.0%
e233
 
7.0%
l228
 
6.8%
g122
 
3.7%
o118
 
3.5%
S107
 
3.2%
t106
 
3.2%
Other values (35)1036
31.1%
ValueCountFrequency (%)
21
87.5%
'3
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3354
99.9%
None3
 
0.1%

Most frequent character per block

ValueCountFrequency (%)
a480
14.3%
n347
 
10.3%
r289
 
8.6%
i267
 
8.0%
e233
 
6.9%
l228
 
6.8%
g122
 
3.6%
o118
 
3.5%
S107
 
3.2%
t106
 
3.2%
Other values (36)1057
31.5%
ValueCountFrequency (%)
ô3
100.0%

Age
Real number (ℝ≥0)

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.972
Minimum19
Maximum39
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:28.493922image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q125
median28
Q331
95-th percentile34
Maximum39
Range20
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.783628906
Coefficient of variation (CV)0.1352648686
Kurtosis-0.4647576836
Mean27.972
Median Absolute Deviation (MAD)3
Skewness0.130311656
Sum13986
Variance14.3158477
MonotocityNot monotonic
2021-03-30T22:46:28.578696image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2953
10.6%
2752
10.4%
2645
9.0%
2844
8.8%
2539
7.8%
3238
7.6%
2337
 
7.4%
3135
 
7.0%
3033
 
6.6%
3330
 
6.0%
Other values (10)94
18.8%
ValueCountFrequency (%)
191
 
0.2%
207
 
1.4%
218
 
1.6%
2217
3.4%
2337
7.4%
ValueCountFrequency (%)
392
 
0.4%
374
 
0.8%
366
1.2%
357
1.4%
3414
2.8%

Height
Real number (ℝ≥0)

Distinct33
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.746
Minimum163
Maximum197
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:28.670482image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum163
5-th percentile170
Q1176
median181
Q3186
95-th percentile191
Maximum197
Range34
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.563203126
Coefficient of variation (CV)0.03631174757
Kurtosis-0.5252059928
Mean180.746
Median Absolute Deviation (MAD)5
Skewness-0.02549150611
Sum90373
Variance43.07563527
MonotocityNot monotonic
2021-03-30T22:46:28.764199image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
18039
 
7.8%
17533
 
6.6%
17828
 
5.6%
18126
 
5.2%
18725
 
5.0%
18525
 
5.0%
18225
 
5.0%
18423
 
4.6%
18323
 
4.6%
18622
 
4.4%
Other values (23)231
46.2%
ValueCountFrequency (%)
1632
 
0.4%
1653
0.6%
1672
 
0.4%
1683
0.6%
1696
1.2%
ValueCountFrequency (%)
1971
 
0.2%
1961
 
0.2%
1955
1.0%
1947
1.4%
1932
 
0.4%

Position
Categorical

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
CB
80 
ST
71 
CM
70 
CDM
46 
CAM
41 
Other values (9)
192 

Length

Max length3
Median length2
Mean length2.184
Min length2

Characters and Unicode

Total characters1092
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowRW
2nd rowST
3rd rowLW
4th rowCAM
5th rowST
ValueCountFrequency (%)
CB80
16.0%
ST71
14.2%
CM70
14.0%
CDM46
9.2%
CAM41
8.2%
LB37
7.4%
RB36
7.2%
LM31
 
6.2%
RM30
 
6.0%
RW22
 
4.4%
Other values (4)36
7.2%
2021-03-30T22:46:28.963666image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cb80
16.0%
st71
14.2%
cm70
14.0%
cdm46
9.2%
cam41
8.2%
lb37
7.4%
rb36
7.2%
lm31
 
6.2%
rm30
 
6.0%
rw22
 
4.4%
Other values (4)36
7.2%

Most occurring characters

ValueCountFrequency (%)
C248
22.7%
M218
20.0%
B158
14.5%
R92
 
8.4%
L89
 
8.2%
S71
 
6.5%
T71
 
6.5%
W47
 
4.3%
D46
 
4.2%
A41
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1092
100.0%

Most frequent character per category

ValueCountFrequency (%)
C248
22.7%
M218
20.0%
B158
14.5%
R92
 
8.4%
L89
 
8.2%
S71
 
6.5%
T71
 
6.5%
W47
 
4.3%
D46
 
4.2%
A41
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin1092
100.0%

Most frequent character per script

ValueCountFrequency (%)
C248
22.7%
M218
20.0%
B158
14.5%
R92
 
8.4%
L89
 
8.2%
S71
 
6.5%
T71
 
6.5%
W47
 
4.3%
D46
 
4.2%
A41
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1092
100.0%

Most frequent character per block

ValueCountFrequency (%)
C248
22.7%
M218
20.0%
B158
14.5%
R92
 
8.4%
L89
 
8.2%
S71
 
6.5%
T71
 
6.5%
W47
 
4.3%
D46
 
4.2%
A41
 
3.8%

Rating
Real number (ℝ≥0)

Distinct15
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.072
Minimum79
Maximum93
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:29.037500image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile79
Q180
median81
Q383.25
95-th percentile87
Maximum93
Range14
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.626139604
Coefficient of variation (CV)0.03199799693
Kurtosis1.274085037
Mean82.072
Median Absolute Deviation (MAD)2
Skewness1.1445322
Sum41036
Variance6.896609218
MonotocityDecreasing
2021-03-30T22:46:29.110861image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
80118
23.6%
8184
16.8%
8368
13.6%
7961
12.2%
8244
 
8.8%
8441
 
8.2%
8534
 
6.8%
8717
 
3.4%
8613
 
2.6%
887
 
1.4%
Other values (5)13
 
2.6%
ValueCountFrequency (%)
7961
12.2%
80118
23.6%
8184
16.8%
8244
 
8.8%
8368
13.6%
ValueCountFrequency (%)
931
 
0.2%
921
 
0.2%
913
0.6%
904
0.8%
894
0.8%

PACE
Real number (ℝ≥0)

Distinct53
Distinct (%)10.7%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean74.6277666
Minimum37
Maximum96
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:29.209552image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile57
Q167
median75
Q382
95-th percentile92
Maximum96
Range59
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.77109116
Coefficient of variation (CV)0.1443308792
Kurtosis0.02703034591
Mean74.6277666
Median Absolute Deviation (MAD)8
Skewness-0.3730775261
Sum37090
Variance116.0164049
MonotocityNot monotonic
2021-03-30T22:46:29.314917image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6823
 
4.6%
7821
 
4.2%
7319
 
3.8%
8119
 
3.8%
8019
 
3.8%
7518
 
3.6%
8317
 
3.4%
7417
 
3.4%
6717
 
3.4%
7216
 
3.2%
Other values (43)311
62.2%
ValueCountFrequency (%)
371
 
0.2%
421
 
0.2%
431
 
0.2%
441
 
0.2%
453
0.6%
ValueCountFrequency (%)
962
 
0.4%
951
 
0.2%
946
1.2%
9310
2.0%
928
1.6%

SHOOTING
Real number (ℝ≥0)

Distinct63
Distinct (%)12.7%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean69.5331992
Minimum25
Maximum93
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:29.546298image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile42.8
Q163
median74
Q379
95-th percentile85
Maximum93
Range68
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.07251042
Coefficient of variation (CV)0.1880038682
Kurtosis0.4276782674
Mean69.5331992
Median Absolute Deviation (MAD)7
Skewness-1.00789575
Sum34558
Variance170.8905287
MonotocityNot monotonic
2021-03-30T22:46:29.654010image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7532
 
6.4%
8027
 
5.4%
7924
 
4.8%
7424
 
4.8%
7624
 
4.8%
7822
 
4.4%
8219
 
3.8%
8117
 
3.4%
7717
 
3.4%
7317
 
3.4%
Other values (53)274
54.8%
ValueCountFrequency (%)
251
0.2%
271
0.2%
281
0.2%
301
0.2%
321
0.2%
ValueCountFrequency (%)
931
0.2%
921
0.2%
912
0.4%
902
0.4%
882
0.4%

PASSING
Real number (ℝ≥0)

Distinct42
Distinct (%)8.5%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean74.74245473
Minimum42
Maximum93
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:29.758762image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile58.8
Q171
median76
Q380
95-th percentile84.2
Maximum93
Range51
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.651564541
Coefficient of variation (CV)0.1023724009
Kurtosis1.023398646
Mean74.74245473
Median Absolute Deviation (MAD)4
Skewness-0.9441798606
Sum37147
Variance58.54643993
MonotocityNot monotonic
2021-03-30T22:46:29.866442image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7839
 
7.8%
7933
 
6.6%
8033
 
6.6%
7632
 
6.4%
7731
 
6.2%
8131
 
6.2%
7229
 
5.8%
7427
 
5.4%
7523
 
4.6%
8220
 
4.0%
Other values (32)199
39.8%
ValueCountFrequency (%)
421
 
0.2%
523
0.6%
531
 
0.2%
544
0.8%
554
0.8%
ValueCountFrequency (%)
931
 
0.2%
912
0.4%
901
 
0.2%
891
 
0.2%
883
0.6%

DRIBBLING
Real number (ℝ≥0)

Distinct41
Distinct (%)8.2%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean78.42052314
Minimum51
Maximum95
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:29.973156image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile62
Q175
median80
Q383
95-th percentile88
Maximum95
Range44
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.511967691
Coefficient of variation (CV)0.09579083881
Kurtosis0.8174580184
Mean78.42052314
Median Absolute Deviation (MAD)4
Skewness-0.9367116343
Sum38975
Variance56.4296586
MonotocityNot monotonic
2021-03-30T22:46:30.073887image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
8242
 
8.4%
8038
 
7.6%
8334
 
6.8%
8133
 
6.6%
7831
 
6.2%
8529
 
5.8%
8423
 
4.6%
7922
 
4.4%
8721
 
4.2%
7520
 
4.0%
Other values (31)204
40.8%
ValueCountFrequency (%)
511
 
0.2%
551
 
0.2%
564
0.8%
571
 
0.2%
581
 
0.2%
ValueCountFrequency (%)
951
 
0.2%
941
 
0.2%
922
 
0.4%
914
 
0.8%
9010
2.0%

DEFENCE
Real number (ℝ≥0)

Distinct65
Distinct (%)13.1%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean63.25754527
Minimum24
Maximum91
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:30.177609image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile31
Q144
median72
Q380
95-th percentile85
Maximum91
Range67
Interquartile range (IQR)36

Descriptive statistics

Standard deviation18.92967856
Coefficient of variation (CV)0.2992477573
Kurtosis-1.326804857
Mean63.25754527
Median Absolute Deviation (MAD)12
Skewness-0.4534566285
Sum31439
Variance358.3327303
MonotocityNot monotonic
2021-03-30T22:46:30.282385image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7728
 
5.6%
8026
 
5.2%
8222
 
4.4%
7921
 
4.2%
7820
 
4.0%
8118
 
3.6%
4317
 
3.4%
7515
 
3.0%
8315
 
3.0%
3914
 
2.8%
Other values (55)301
60.2%
ValueCountFrequency (%)
242
 
0.4%
252
 
0.4%
273
 
0.6%
294
 
0.8%
3010
2.0%
ValueCountFrequency (%)
911
 
0.2%
901
 
0.2%
892
 
0.4%
882
 
0.4%
878
1.6%

PHYSICAL
Real number (ℝ≥0)

Distinct41
Distinct (%)8.2%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean73.22334004
Minimum44
Maximum91
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-03-30T22:46:30.384057image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile58
Q168
median75
Q380
95-th percentile85
Maximum91
Range47
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.315624777
Coefficient of variation (CV)0.1135652208
Kurtosis-0.1550847136
Mean73.22334004
Median Absolute Deviation (MAD)6
Skewness-0.6166689679
Sum36392
Variance69.14961543
MonotocityNot monotonic
2021-03-30T22:46:30.493318image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
7729
 
5.8%
7629
 
5.8%
7926
 
5.2%
7825
 
5.0%
8024
 
4.8%
8124
 
4.8%
7321
 
4.2%
8320
 
4.0%
7520
 
4.0%
6920
 
4.0%
Other values (31)259
51.8%
ValueCountFrequency (%)
441
 
0.2%
502
0.4%
511
 
0.2%
522
0.4%
533
0.6%
ValueCountFrequency (%)
911
 
0.2%
882
 
0.4%
871
 
0.2%
868
1.6%
8515
3.0%

Interactions

2021-03-30T22:46:20.284194image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:20.386919image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:20.485687image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:20.579404image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:20.672155image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:20.764907image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:20.854700image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:20.953405image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.107153image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.207883image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.297676image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.396380image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.502095image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.591855image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.679651image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.769417image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.860138image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:21.965886image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.053620image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.145401image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.252151image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.344906image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.442154image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.531913image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.622671image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.711435image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.798233image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.883972image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:22.967826image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.050526image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.136329image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.220767image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.298893image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.394605image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.558200image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.644936image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.732702image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.816176image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.898959image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:23.990676image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.074484image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.165210image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.249022image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.331764image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.418534image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.506329image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.588078image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.675843image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.756657image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.843395image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:24.935554image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.018627image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.103400image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.183240image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.261976image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.347746image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.430603image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.522355image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.608128image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.696860image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.781664image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.872389image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:25.966139image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.048917image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.133690image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.220458image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.392059image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.478853image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.562876image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.647689image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.737410image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2021-03-30T22:46:26.815232image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Correlations

2021-03-30T22:46:30.583078image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-30T22:46:30.709257image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-30T22:46:30.833924image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-30T22:46:30.966600image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-30T22:46:31.105197image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-30T22:46:26.997808image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-30T22:46:27.204253image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-30T22:46:27.338893image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-30T22:46:27.437629image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

NameClubLeagueNationAgeHeightPositionRatingPACESHOOTINGPASSINGDRIBBLINGDEFENCEPHYSICAL
0Lionel MessiFC BarcelonaSpain Primera DivisiónArgentina33170RW9385.092.091.095.038.065.0
1C. Ronaldo dos Santos AveiroPiemonte CalcioItaly Serie APortugal35187ST9289.093.081.089.035.077.0
2Neymar da Silva Santos Jr.Paris Saint-GermainFrance Ligue 1Brazil28175LW9191.085.086.094.036.059.0
3Kevin De BruyneManchester CityEngland Premier LeagueBelgium29181CAM9176.086.093.088.064.078.0
4Robert LewandowskiBayern MünchenGermany 1. BundesligaPoland32184ST9178.091.078.086.043.082.0
5Sadio ManéLiverpoolEngland Premier LeagueSenegal28175LW9094.085.080.090.044.076.0
6Mohamed SalahLiverpoolEngland Premier LeagueEgypt28175RW9093.086.081.090.045.075.0
7Kylian MbappéParis Saint-GermainFrance Ligue 1France21178ST9096.086.078.091.039.076.0
8Virgil van DijkLiverpoolEngland Premier LeagueHolland29193CB9076.060.071.072.091.086.0
9Carlos Henrique Venancio CasimiroReal MadridSpain Primera DivisiónBrazil28185CDM8965.073.076.073.086.091.0

Last rows

NameClubLeagueNationAgeHeightPositionRatingPACESHOOTINGPASSINGDRIBBLINGDEFENCEPHYSICAL
490Jaume Vicent Costa JordáVillarreal CFSpain Primera DivisiónSpain32171LB7975.061.072.073.078.070.0
491Nicolás OtamendiManchester CityEngland Premier LeagueArgentina32183CB7954.057.061.059.080.078.0
492Chris SmallingManchester UnitedEngland Premier LeagueEngland30194CB7968.047.058.059.081.081.0
493Diego PerottiGeneric CapitaleItaly Serie AArgentina32179LM7974.071.077.085.042.054.0
494Marcelo DíazRacing Club de AvellanedaCONMEBOL LibertadoresChile33167CDM7959.066.080.075.074.074.0
495Kévin GameiroValencia CFSpain Primera DivisiónFrance33172ST7986.079.074.076.043.067.0
496Sokratis PapastathopoulosArsenalEngland Premier LeagueGreece32186CB7967.053.052.060.078.081.0
497Daniel Olmo CarvajalRB LeipzigGermany 1. BundesligaSpain22179CAM7968.076.077.082.050.062.0
498Marcus Wendel Valle da SilvaSporting CPPortugal Primeira LigaBrazil23180CM7978.075.077.080.073.071.0
499Igor Zubeldia ElorzaReal SociedadSpain Primera DivisiónSpain23181CDM7965.067.076.072.075.080.0